What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Deep learning enabled motion detection in quantitative macromolecule proton faction mapping in the liver

Key Points

The automated motion detection model achieved an accuracy of 86.4%, improving MPF-SL quantification reliability.
AUC statistics showed good performance with a value of 0.79, indicating robust detection of liver motion.
The deep learning model helps in either discarding unreliable measurements or prompting data recollection during scans.
This approach leverages the MT effect to sensitively assess macromolecule content in the liver, enhancing measurement precision.

Abstract

Motivation: Macromolecular proton fraction (MPF) quantification based on spin-lock (MPF-SL) is a technology, which sensitively measures macromolecule content using the MT effect. However, the motion of liver can lead to inaccurate MPF-SL quantification. Goal(s): Develop an automated processing approach that can detect motion of the liver to reduce the impact on MPF-SL quantification. Approach: We trained a deep learning model to automatically detect the motion of the liver during MPF-SL acquisition. Results: The proposed model demonstrated good performance with an accuracy of 86.4% and an area under the receiver operating characteristic curve (AUC) of 0.79. Impact: Our approach enables automated motion detection of the liver during MPF-SL scan. It can improve reliability of parameter quantification by either discarding unreliable measurements retrospectively or prompt data recollection prospectively during scanning.

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